Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Computer Vision – ECCV 2020 16th European Conference, 2020, 12365 LNCS, pp. 240-256
Issue Date:
2020-01-01
Filename Description Size
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior | OpenReview.pdfSupporting information114.71 kB
Adobe PDF
2003.07637v2.pdfAccepted version7.61 MB
Adobe PDF
Full metadata record
© 2020, Springer Nature Switzerland AG. Deep neural networks are known to be susceptible to adversarial noise, which is tiny and imperceptible perturbation. Most of previous works on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored. In this paper, we aim to attack video models by utilizing intrinsic movement pattern and regional relative motion among video frames. We propose an effective motion-excited sampler to obtain motion-aware noise prior, which we term as sparked prior. Our sparked prior underlines frame correlations and utilizes video dynamics via relative motion. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries. Extensive experimental results on four benchmark datasets validate the efficacy of our proposed method.
Please use this identifier to cite or link to this item: